Optimalisasi Parameter dengan Cross Validation dan Neural Back-propagation Pada Model Prediksi Pertumbuhan Industri Mikro dan Kecil

Agus Perdana Windarto, Sarjon Defit, Anjar Wanto
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引用次数: 2

Abstract

It is important for us to predict what will happen in future and to reduce uncertainty. Various analyzes are therefore necessary in order to optimize or improve the prediction results by several methods. The objective of this research is to analyze predictive results by optimizing the training and testing by means of cross validating parameters on the growth of micro and small-scale production in Indonesia through the exactness of the return-propagative method. The method of reproduction is used. These results are compared with results of backpropagation during training and testing without optimisation of the same architectural model. The dataset is based on the growth in production in micro and small businesses by province from the Central Statistical Agency(BPS). There were 34 records in which data from 2015-2019 for growth of production were collected. The results with optimisation have surpassed without optimisation the back propagation model by looking at RMSE, in which the best RMSE in the 3-2-1 architectural model was obtained and the side type is mixed sampling. The obtained RMSE value is 0.1526, or a difference between the best background architectural model, 3-2-1 and 0.0034. (0.157). The results of this model were 94 percent.
预测未来会发生什么并减少不确定性对我们来说很重要。因此,为了优化或改进几种方法的预测结果,需要进行各种分析。本研究的目的是通过回归传播法的准确性,通过交叉验证参数对印度尼西亚微型和小规模生产生长进行优化训练和测试,分析预测结果。采用繁殖的方法。这些结果与未对同一架构模型进行优化的训练和测试期间的反向传播结果进行了比较。该数据集基于中央统计局(BPS)各省微型和小型企业的生产增长。有34条记录收集了2015-2019年的产量增长数据。通过观察RMSE,优化的结果超过了没有优化的反向传播模型,其中获得了3-2-1架构模型中的最佳RMSE,并且侧类型为混合采样。得到的RMSE值为0.1526,即最佳背景架构模型3-2-1与0.0034的差值。(0.157)。该模型的结果为94%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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